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okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

okcupid-stem_seed_4_nrows_2000_nclasses_10_ncols_100_stratify_True

active ARFF Publicly available Visibility: public Uploaded 17-11-2022 by Eddie Bergman
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Subsampling of the dataset okcupid-stem (42734) with seed=4 args.nrows=2000 args.ncols=100 args.nclasses=10 args.no_stratify=True Generated with the following source code: ```python def subsample( self, seed: int, nrows_max: int = 2_000, ncols_max: int = 100, nclasses_max: int = 10, stratified: bool = True, ) -> Dataset: rng = np.random.default_rng(seed) x = self.x y = self.y # Uniformly sample classes = y.unique() if len(classes) > nclasses_max: vcs = y.value_counts() selected_classes = rng.choice( classes, size=nclasses_max, replace=False, p=vcs / sum(vcs), ) # Select the indices where one of these classes is present idxs = y.index[y.isin(classes)] x = x.iloc[idxs] y = y.iloc[idxs] # Uniformly sample columns if required if len(x.columns) > ncols_max: columns_idxs = rng.choice( list(range(len(x.columns))), size=ncols_max, replace=False ) sorted_column_idxs = sorted(columns_idxs) selected_columns = list(x.columns[sorted_column_idxs]) x = x[selected_columns] else: sorted_column_idxs = list(range(len(x.columns))) if len(x) > nrows_max: # Stratify accordingly target_name = y.name data = pd.concat((x, y), axis="columns") _, subset = train_test_split( data, test_size=nrows_max, stratify=data[target_name], shuffle=True, random_state=seed, ) x = subset.drop(target_name, axis="columns") y = subset[target_name] # We need to convert categorical columns to string for openml categorical_mask = [self.categorical_mask[i] for i in sorted_column_idxs] columns = list(x.columns) return Dataset( # Technically this is not the same but it's where it was derived from dataset=self.dataset, x=x, y=y, categorical_mask=categorical_mask, columns=columns, ) ```

20 features

job (target)nominal3 unique values
0 missing
agenumeric52 unique values
0 missing
body_typenominal12 unique values
174 missing
dietnominal14 unique values
761 missing
drinksnominal6 unique values
44 missing
drugsnominal3 unique values
471 missing
educationnominal28 unique values
139 missing
ethnicitynominal60 unique values
175 missing
heightnumeric27 unique values
0 missing
incomenominal11 unique values
1575 missing
locationnominal65 unique values
0 missing
offspringnominal15 unique values
1103 missing
orientationnominal3 unique values
0 missing
petsnominal15 unique values
594 missing
religionnominal42 unique values
585 missing
sexnominal2 unique values
0 missing
signnominal48 unique values
301 missing
smokesnominal5 unique values
128 missing
speaksnominal531 unique values
0 missing
statusnominal5 unique values
0 missing

19 properties

2000
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
6050
Number of missing values in the dataset.
1910
Number of instances with at least one value missing.
2
Number of numeric attributes.
18
Number of nominal attributes.
5
Percentage of binary attributes.
95.5
Percentage of instances having missing values.
0.55
Average class difference between consecutive instances.
15.13
Percentage of missing values.
0.01
Number of attributes divided by the number of instances.
10
Percentage of numeric attributes.
71.6
Percentage of instances belonging to the most frequent class.
90
Percentage of nominal attributes.
1432
Number of instances belonging to the most frequent class.
9.6
Percentage of instances belonging to the least frequent class.
192
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

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